Network Traffic Prediction Model Based on Catfish-PSO-SVM

In order to improve the prediction accuracy of network traffic, this paper proposes a network traffic prediction model based on support vector machine (SVM) which parameters are optimized by catfish particle swarm optimization algorithm. Firstly, the parameters of SVM are encoded as a particle, and then catfish effect is introduced to overcome the defects of particle swarm optimization algorithm, the optimal parameters of SVM are obtained by the particle interactions, finally, the prediction model of network traffic is established according to the optimal parameters and the simulation experiments are carried out to test the performance of established model with network traffic data. The simulation results show that, compared with other prediction models of network traffic, the proposed model can quickly find the optimal parameters of SVM and has improved the prediction accuracy of network traffic.